System and method for providing an adjusted weighted repeat sale index

ABSTRACT

Systems, methods, and computer-readable storage media are described for estimating real estate property values based on an adjusted repeat sales model. In one exemplary embodiment, a computer-implemented method comprises calculating data for estimating the adjustments from aggregated levels to disaggregated levels by marking a first transaction to a second transaction using a repeat sales house price index function at an aggregated level; determining, using the calculated data, an estimate of the deviation between the repeat sales house price index at the aggregated level and a repeat sales house price index at a disaggregated level; and calculating the repeat sales house price index at the disaggregated level based on the determined estimate of the deviation from the aggregated level.

TECHNICAL FIELD

The present disclosure generally relates to estimating property values,and more particularly, to providing property value estimates based on arepeat sales model.

BACKGROUND

Financial institutions and businesses involved with sales of propertyhave long tried to estimate values of property accurately. Accurateestimation serves many important purposes. For example, financialinstitutions use property value estimates as one of the key factors incalculating the loan to value (LTV) ratio of a home. The LTV ratio isthe ratio of a first mortgage to the appraised value of the realproperty. The LTV ratio is an important calculation used by financialinstitutions to assess lending risks. For example, as the LTV ratio of aproperty increases, the likelihood of loan default increases. Inaddition, when a default does occur, the higher the LTV ratio, thegreater the potential financial loss to the financial institution.Moreover, financial institutions may use the LTV ratio to mark-to-markettheir portfolio of outstanding loans. Mark-to-market is an accountingmethodology used to calculate current LTV ratio of outstanding loans.Accordingly, the accuracy of the estimated value of real estate neededto calculate the LTV ratio is critical.

One technique for attempting to obtain an accurate estimated value ofreal estate utilizes a repeat sales index. A repeat sales index may beused to identify housing market conditions and the amount of equityhomeowners have gained through house price appreciation. The indexitself is a composite of changes for individual house prices within ageographical region, such as a municipality, zip code, county, region,or state. The data used in the repeat sales index may comprisesuccessive selling prices and the sale dates for the same property(e.g., residential house). In essence, this approach finds the averagerate of property appreciation in each period that gives the beststatistical fit to all the overlapping holding periods. By using pricingof the same property, the repeat sales index eliminates the bias inprice changes that are not due to the true house price change, but dueto external factors such as, for example, consumer trends for biggerhouses.

The basic repeat sales index may be improved through the use of datafrom refinance transactions, in addition to data from purchasetransactions, in forming repeat sales forecasts, thereby increasing thesize of the estimation sample and the timeliness of the evaluationsample. Moreover, as disclosed in U.S. Pat. No. 6,401,070, the data usedin a repeat sales index may be weighted to provide particular importanceto one set of data over another. The content of U.S. Pat. No. 6,401,070is incorporated herein by reference.

There are qualitative differences between house price data derived frompurchase transactions and from refinance transactions. Purchasetransactions typically involve arms-length agreements in which theincentives of the parties will tend to result in an unbiased salesprice, and the information of the three parties (buyer, seller, andappraiser) will tend to result in greater accuracy in ascertaining thevalue of the property. Refinance transactions, on the other hand, havevaluation based solely on an appraisal and consequently are subject toseveral sources of bias. For example, incentive biases in appraisalsarise because appraisers are motivated to arrive at valuations that canmake the refinance transaction successful. Selection biases arisebecause, particularly in a down market, the properties that are eligiblefor refinance are more likely to be those that have appreciated relativeto the market as a whole. Accordingly, a repeated sales index thatfactors in biases to the data is referred to as a weighted repeat salesindex (WRSI). Here WRSI is used generically. WRSI also refers to indexesthat include refinance transactions as well as property saletransactions, and indexes with and without weights on the transactions.As disclosed in U.S. Pat. No. 6,401,070, the WRSI may be expressed as:log(P _(s) /P _(t))=I _(s) −I _(t) +d _(s2) R _(s2) −d _(t1) R _(t1)+ξ

The variable P_(t) is the first transaction price, P_(s) is the secondtransaction price, I_(t) is the log index value at time t, R_(t1) isequal to one (1) if the first transaction is a refinance and equal tozero (0) otherwise, R_(s2) is equal to one (1) if the second transactionis a refinance and equal to zero (0) otherwise, d_(t1) is a coefficientrepresenting the first transaction refinance bias at time t, d_(s2) iscoefficient representing the second transaction refinance bias at times, and ξ is the error term. In essence, the refinance bias terms measurethe difference in appreciation between purchase and refinancetransactions at the two dates. The d_(t1) coefficients may be thought ofas measuring the incentive bias and the d_(s2) coefficients as measuringthe combined selection and incentive bias. Accordingly the WRSI model ofequation (1) allows for time varying differences between refinance andpurchase transactions, thereby improving forecast accuracy.

As used herein, “aggregated level” refers to a geographic regioncomprised of smaller geographic regions. For example, a state may be anaggregated level of counties. As used herein, “disaggregated level”refers to a geographic region that may be included in an aggregatedlevel. For example, a county may be a disaggregated level of a state.

Using the WRSI model, trends and growth changes in house prices may beevident from examining plots of quarterly WRSI growth at aggregatedlevels, such as a state level and region level (e.g., one of thegeographic regions within the United States of America officiallyrecognized by the United States Census Bureau). However, trends andgrowth changes in house prices are generally not evident when examiningquarterly plots of WRSI growth at disaggregated levels, such as at acounty level, a zip code level, or a census tract. This disparity iscaused primarily by the occurrence of relative fewer transactions (i.e.,purchases, refinances) at a disaggregated level when compared to anaggregated level. Accordingly, when examining WRSI at a disaggregatedlevel, a proper analysis of market conditions and trends is not possibleover relatively short periods of time. For example, in a volatilehousing market, housing prices in a large geographic region, such as thestate of California, may fall in one quarter by 10%. However, examiningthe WRSI within a zip code in California over the same quarterly ormonthly period may not demonstrate a fall of 10%. This is due to therelative lower number of transactions at the zip code level. In fact,there may be very few, or even no transactions in the zip code in thequarter or month.

Accordingly, the inventors have determined that WRSI may lag inproviding property growth rate estimates at disaggregated geographiclevels, and may not exhibit seasonal differences in property values.Systems and methods consistent with the present invention address thedifficulties discussed above by providing an adjusted WRSI thatcalculates a more accurate estimated value of real estate growth ratesat disaggregated levels, among other things.

SUMMARY

Consistent with the present invention, as embodied and broadly describedherein, systems and methods are disclosed for providing an adjustedweighted repeat sales index.

In one exemplary embodiment, a method for adjusting a weighted repeatsales index is disclosed. The method comprises calculating data forestimating the adjustments from aggregated levels to disaggregatedlevels by marking a first transaction to a second transaction using arepeat sales house price index function at an aggregated level;determining, using the calculated data, an estimate of the deviationbetween the repeat sales house price index at the aggregated level and arepeat sales house price index at a disaggregated level; and calculatingthe repeat sales house price index at the disaggregated level based onthe determined estimate of the deviation from the aggregated level.

In another embodiment, a system for adjusting a weighted repeat salesindex is disclosed. The system comprises means for calculating data forestimating the adjustments from aggregated levels to disaggregatedlevels by marking a first transaction to a second transaction using arepeat sales house price index function at an aggregated level; meansfor determining, using the calculated data, an estimate of the deviationbetween the repeat sales house price index at the aggregated level and arepeat sales house price index at a disaggregated level; and means forcalculating the repeat sales house price index at the disaggregatedlevel based on the determined estimate of the deviation from theaggregated-level index.

In yet another embodiment, a computer-readable medium including programinstructions for performing, when executed by a processor, a method foradjusting a weighted repeat sales index. The method comprisescalculating data for estimating the adjustments from aggregated levelsto disaggregated levels by marking a first transaction to a secondtransaction using a repeat sales house price index function at anaggregated level; determining, using the calculated data, an estimate ofthe deviation between the repeat sales house price index at theaggregated level and a repeat sales house price index at a disaggregatedlevel; and calculating the repeat sales house price index at thedisaggregated level based on the determined estimate of the deviationfrom the aggregated-level index.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the invention, as described. Further featuresand/or variations may be provided in addition to those set forth herein.For example, embodiments of the present invention may be directed tovarious combinations and subcombinations of several further featuresdisclosed below in the detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various features, embodiments andaspects consistent with the invention and, together with thedescription, explain advantages and principles of the invention. In thedrawings,

FIG. 1 is a block diagram of an exemplary overview of a property valueestimation system, consistent with the principles of the presentinvention;

FIGS. 2A and 2B illustrate examples of plots of the WRSI quarterly houseprice growth at aggregated levels, consistent with the principles of thepresent invention;

FIGS. 3A and 3B illustrate examples of plots of the WRSI quarterly houseprice growth at disaggregated levels, consistent with the principles ofthe present invention;

FIG. 4 is an exemplary flowchart for an adjusted WRSI methodology,consistent with the principles of the present invention;

FIG. 5 is an exemplary table of measured performance statistics foradjusted WRSI and WRSI, consistent with the principles of the presentinvention; and

FIGS. 6A and 6B illustrate examples of plots of adjusted WRSI and WRSIquarterly house price growth at two examples of disaggregated levels.

DESCRIPTION OF THE EMBODIMENTS

Reference will now be made in detail to various embodiments of theinvention, examples of which are illustrated in the accompanyingdrawings. Wherever convenient, similar reference numbers will be usedthroughout the drawings to refer to the same or like parts. Theimplementations set forth in the following description do not representall implementations consistent with the claimed invention. Instead, theyare merely some examples of systems and methods consistent with theinvention.

Systems and methods consistent with principles of the present inventionaddress the limitations and disadvantages of traditional WRSI forforecasting house price values. Systems and methods consistent withprinciples of the present invention estimate real estate property valuesbased on an adjusted repeat sales model.

FIG. 1 is a block diagram illustrating an exemplary system architecturefor a computer system with which embodiments consistent with the presentinvention may be implemented. In the embodiment shown, computer system100 includes a bus 102 or other communication mechanism forcommunicating information, and a processor 104 coupled with bus 102 forprocessing information. Computer system 100 also includes a main memory,such as a random access memory (RAM) 106 or other dynamic storagedevice, coupled to bus 102 for storing information and instructions tobe executed by processor 104. RAM 106 also may be used to storetemporary variables or other intermediate information produced duringexecution of instructions by processor 104. Computer system 100 furtherincludes a read only memory (ROM) 108 or other static storage devicecoupled to bus 102 for storing static information and instructions forprocessor 104. A storage device 110, such as a magnetic disk or opticaldisk, is provided and coupled to bus 102 for storing information andinstructions.

Computer system 100 may be coupled via bus 102 to a display 112, such asa thin film transistor liquid crystal display (TFT-LCD), for displayinginformation to a computer user. An input device 114, such as a keyboardincluding alphanumeric and other keys, is coupled to bus 102 forcommunicating information and command selections to processor 104.Another type of user input device is a cursor control 116, such as amouse, a trackball, or cursor direction keys for communicating directioninformation and command selections to processor 104 and for controllingcursor movement on display 112. This input device typically has twodegrees of freedom in two axes, a first axis (e.g., x) and a second axis(e.g., y), that allows the device to specify positions in a plane.

In the embodiment show, computer system 100 accesses data from realestate database 130 and executes one or more sequences of one or moreinstructions contained in main memory 106. Both the data from realestate database 130 and the instructions may be read into main memory106 from another computer-readable medium, such as storage device 110.Data from real estate database 130 may comprise refinance and purchasetransaction data 132, nonparametric estimation data 134, andnonparametric with refinance and purchase transaction data 136. Theinstructions may implement adjusted WRSI models, as discussed in greaterdetail below. Execution of the sequences of instructions contained inmain memory 106 causes processor 104 to perform operations consistentwith the process steps described herein. In one alternativeimplementation, hardwired circuitry may be used in place of or incombination with real estate database and/or software instructions toimplement the invention. Thus implementations of the invention are notlimited to any specific combination of hardware circuitry and software.

Computer system 100 may communicate with real estate database 130through a communication channel comprising, for example, alone or in anysuitable combination, a telephony-based network, a local area network(LAN), a wide area network (WAN), a dedicated intranet, wireless LAN,the Internet, and intranet, a wireless network, a bus, or otherappropriate communication mechanisms. Moreover, various combinations ofwired and/or wireless components may be incorporated into thecommunication channel. Furthermore, various combinations ofpoint-to-point or network communications may also be incorporated intothe communication channel to facilitate communication between thecomputer system 100 and the real estate database 130. Additionally, datacommunicated through the communication channel may be communicatedinstead through the transfer of computer-readable media, such as DVDs.

The term “computer-readable medium” as used herein refers to any mediathat participates in providing instructions to processor 104 forexecution. Such a medium may take many forms, including but not limitedto, non-volatile media and volatile media. Non-volatile media includes,for example, optical or magnetic disks, such as storage device 110.Volatile media includes dynamic memory, such as main memory 106.

FIGS. 2A and 2B illustrate examples of plots of WRSI quarterly houseprice growth at aggregated levels using equation (1) disclosed above.Specifically, FIG. 2A illustrates a WRSI growth plot of the Northeastregion of the United States and FIG. 2B illustrates a plot of the stateof Virginia from the first quarter of 1984 through the first quarter of2006.

FIGS. 3A and 3B illustrate examples of plots, over the same time periodas FIGS. 2A and 2B, of WRSI quarterly house price growth atdisaggregated levels using equation (1). Specifically, FIG. 3Aillustrates a WRSI growth plot of Arlington County in the state ofVirginia and FIG. 3B illustrates a WRSI growth plot for the zip code22207 located within Arlington County. When comparing the plots of FIGS.2A and 2B with the plots of FIGS. 3A and 3B, it is evident that patterns(e.g., the seasonality of home prices) and trends (e.g., a decline inthe overall housing market) in housing prices are more readily detectedat aggregated levels than at disaggregated levels. A primary factor inthe differences in the plots of FIGS. 2A and 2B in comparison with FIGS.3A and 3B is that fewer quarterly knot points are selected (i.e., fewerchanges in growth rates are included) and used in the WRSI model atdisaggregated levels since there are fewer observations (i.e., purchasesand refinances) available for estimation.

In one embodiment consistent with the invention, to improve WRSI plotsat disaggregated levels, information from the more aggregated levels maybe incorporated into the WRSI model in order to provide an estimate ofthe disaggregated levels as deviations from the aggregated levels. Thisimproved WRSI model is referred to herein as the “adjusted WRSI model”or “adjusted WRSI.”

FIG. 4 is an exemplary flowchart of the steps used to implement anembodiment of the adjusted WRSI model. In step 410, data for estimatingthe adjustments from aggregated to disaggregated levels are created bymarking (e.g., pairing) a first real estate transaction to a second realestate transaction using a repeat sales house price index function at anaggregated level, designated by I(t). For discussion of this embodiment,the aggregated level will be the state level. I(t) for the state levelmay be calculated using equation (1) discussed above. Specifically, inone embodiment, I(t) may be calculated using equation (1) in conjunctionwith either standard regression techniques and refinance and purchasedata 132 or nonparametric estimation techniques with nonparametricestimation data 134 or nonparametric with refinance and purchase data136. Nonparametric estimation techniques improve over standardregression techniques by removing temporal aggregation bias, whichresults from treating house price inflation as a constant within a timeinterval. In addition, nonparametric estimation techniques take as fixedthe tradeoff between the variability of the estimates and the bias inthe fineness of time intervals used. For example, it may be better touse wider intervals over time periods with relatively few observationsand conversely, tighter intervals over time periods with a large numberof observations. In an embodiment using a linear spline nonparametricestimator, I(t) may be approximated as shown in equation (2):I(t)=a+b ₀ t+b ₁max[0,t−kj ₁ ]+b ₂max[0,t−k ₂ ] . . . b _(n)max[0,t−k_(n)]  (2)with knot points at (k₁, . . . , k_(n)) and coefficients (a, b₀, . . . ,b_(n)). An implementation of a nonparametric functional estimator may bereferred to as a linear regression spline, which estimates the unknownindex function I(t). As the number of knots increases (n→∞) and{k_(i)}^(n) _(i=1) becomes dense in the domain of the function, theapproximating class of I_(s) has the property that over compact domains,min_(b)∥I_(s)(t)−I(t)∥→0. And thus under suitable technical conditions,if the number of known knot points is allowed to increase with samplesize, I(t) can be consistently estimated.

In step 420, the second step shown in FIG. 4, estimation of thedeviation between the aggregated level index, in this example the statelevel index, and the disaggregated level index, is determined. Forpurposes of this example, the disaggregated level index is the countylevel. The deviations may be determined using time spline variables. Thetime spline variables can be those described in U.S. Pat. No. 6,401,070“System and Method for Providing House Price Forecasts based on RepeatSales Model.” The marked transactions from step 410, along with theaggregate-level (state-level) index are the inputs to step 420. When thedeviations are estimated in step 420, they are then passed to step 430.

In step 430, an adjusted WRSI for the disaggregated level is created byadjusting the aggregated level index, using the adjustments calculatedin step 420. In this example, an adjusted WRSI for the county level iscreated by calculating the repeat sales house price index function I(t)for the state by the estimated deviations determined in step 420. Theadjusted WRSI model may be expressed as:

$\begin{matrix}{{\ln\left( \frac{P_{i{({t + 1})}}}{{\hat{P}}_{i{({t + 1})}}} \right)} = {{\sum\limits_{j = 1}^{k}{\beta_{j}\left\lbrack {{\max\left( {0,{{date}_{i{({t + 1})}} - s_{j}}} \right)} - {\max\left( {0,{{date}_{it} - s_{j}}} \right)}} \right\rbrack}} + {\beta_{({k + 1})}R_{it}} + {\beta_{({k + 2})}R_{i{({t + 1})}}} + {\sum\limits_{j = 1}^{k}{\delta_{j}{\max\left( {0,{{date}_{it} - s_{j}}} \right\}}R_{it}}} + {\sum\limits_{j = 1}^{k}{\varphi_{j}{\max\left( {0,{{date}_{i{({t + 1})}} - s_{j}}} \right)}R_{i{({t + 1})}}}} + {\mathbb{e}}_{{it}{({t + 1})}}}} & (3)\end{matrix}$

The variable P_(i(t+1)) is the transaction value (i.e., the purchaseprice or appraised value) of house i (i=1, . . . , n) at time t+1 (t=1,. . . , T), {circumflex over (P)}_(i(t+1)) is the estimated transactionvalue (i.e., estimated purchase price or estimated appraisal value) ofhouse i (i=1, . . . , n) at time t+1 (t=1, . . . , T), estimated as thevalue of house i at time t inflated/deflated to time t+1 according tothe state level index, date_(it) is the purchase or refinance date ofhouse i (i=1, . . . , n) at time t (t=1, . . . , T), R_(it) is therefinance flag (0=purchase, 1=refinance) for transaction of house i(i=1, . . . , n) at time t (t=1, . . . , T), S_(j) is the knot point(specified as a date) for the j^(th) spline variable (j=1, . . . , k,where k is the number of quarters between 1975Q1 and current quarter),and βj, β_((k+1)), β_((k+2)) δj, φj are the model parameters (j=1, . . ., k, where k is the number of quarters between a starting quarter, suchas 1975Q1 and a current quarter).

The above adjusted WRSI model not only calculates an adjusted index of adisaggregated level using data from that disaggregated level, but alsousing data from an aggregated level that contains the disaggregatedlevel. For example, the above model may calculate an index for aparticular zip code using both data from that particular zip code leveland data from the state in which the zip code is located. As a result,the adjusted WRSI model estimates the index of a disaggregated level asa deviation from an aggregated level.

In one embodiment, equation (3) may be simplified by eliminatingrefinance transactions and thereby using only purchase price data astransaction values. Such a simplification may be expressed as shown inequation (4):

$\begin{matrix}{{\ln\left( \frac{P_{i{({t + 1})}}}{{\hat{P}}_{i{({t + 1})}}} \right)} = {{\sum\limits_{j = 1}^{k}{\beta_{j}\left\lbrack {{\max\left( {0,{{date}_{i{({t + 1})}} - s_{j}}} \right)} - {\max\left( {0,{{date}_{it} - s_{j}}} \right)}} \right\rbrack}} + {\mathbb{e}}_{{it}{({t + 1})}}}} & (4)\end{matrix}$Again, maintaining the example where the aggregated level is a statelevel and the disaggregated level is a county level, the county levelindex is estimated as a deviation from the state level index. Thus, forany repeat sales observation of a specific property (P₁=earlier saleprice, P₂=recent sale price) in the county level regression, the changein purchase price is expressed as a deviation of the property's pricegrowth relative to the state index growth. The difference between theestimated purchase value and the actual purchase value (i.e.,“residual”) is given by log(P₂)−log(P₁)−(I₂ ^(state)−I₁ ^(state)). Usingthis residual as the dependent variable, the log index for the countymay be determined by the sum of the state log index plus the estimatedaverage deviations from the state index. This may be represented asI^(county)=I^(state)+I^(county deviation from state).

FIG. 5 illustrates an exemplary comparison of measured performancestatistics for WRSI and adjusted WRSI as disclosed herein. In theimplementation illustrated, in the overall comparison between the fourthquarter of 2005 through the fourth quarter of 2006 (row 505), the use ofWRSI in a mark-to-market value estimation process results in a medianbias of 1.1% above actual sales price (cell 510, top number). On theother hand, using adjusted WRSI in a mark-to-market value estimationprocess results in a median bias of only 0.3% above actual sales price(cell 510, bottom number). As further illustrated in FIG. 5, using WRSIin the mark-to-market value estimation process results in 56.9% of valueestimates falling within ±10% of the actual sales price, while the useof adjusted WRSI in the mark-to-market value estimation process producesan improved result of 57.5% of value estimates falling within ±10% ofthe actual sales price (cell 515). WRSI also produces a result of 82.9%of value estimates falling within ±20% of the actual sales price, whileadjusted WRSI produces an improved result of 83.3% of value estimatesfalling within ±20% of the actual sales price (cell 520). A calculationof the Robust Root Means Squared Error (RMSE), which includes the biasplus the standard deviation, illustrates that adjusted WRSI produces alower robust RMSE of 12.3% when compared to 12.5% of WRSI (cell 525).FIG. 5 also illustrates measurements of performance statistics ofadjusted WRSI and WRSI for specific quarters (rows 530-570).

FIG. 6A illustrates exemplary plots of adjusted WRSI and WRSI at adisaggregated level, in this example, for Arlington county. FIG. 6Billustrates exemplary plots of adjusted WRSI and WRSI index growth ratesat another disaggregated level, in this example, for zip code 22207. Asshown in these figures, adjusted WRSI produces an improved indication ofchanges in home price values when compared with WRSI. For example, inFIG. 6B, the seasonal trends from 1999 through 2002 are clearly shown inthe adjusted WRSI plot, but the standard WRSI plot for the same periodis essentially flat, indicating no seasonal changes.

The foregoing description of possible implementations and embodimentsconsistent with the present invention does not represent a comprehensivecatalog of all such implementations or all variations of theimplementations described. The description of only some implementationsshould not be construed as an intent to exclude other implementations.Other embodiments of the invention will be apparent to those skilled inthe art from consideration of the specification and practice of theinvention disclosed herein. One of ordinary skill in the art willunderstand how to implement the invention in the appended claims inother ways using equivalents and alternatives that do not depart fromthe scope of the following claims. It is intended that the specificationand examples be considered as exemplary only, with a true scope andspirit of the invention being indicated by the following claims.

What is claimed is:
 1. A computer-implemented method for adjusting aweighted repeat sales index using at least one processor, the methodcomprising: calculating, by the at least one processor, data forestimating adjustments from aggregated levels to disaggregated levels bymarking a data element representing a first transaction to a dataelement representing a second transaction using a processor-implementedrepeat sales house price index function at an aggregated level;determining, by the at least one processor using the calculated data, anestimate of deviation between the repeat sales house price index at theaggregated level and a repeat sales house price index at a disaggregatedlevel; and calculating, by the at least one processor, the repeat saleshouse price index at the disaggregated level based on the determinedestimate of the deviation from the aggregated level, wherein theadjusted weighted repeat sales index produces an estimated indexfunction.
 2. The computer-implemented method of claim 1, wherein thedeviations are estimated as a function of time spline variables.
 3. Thecomputer-implemented method of claim 2, wherein an increase of a numberof knot points of the time spline increases the accuracy of theestimated index function.
 4. The computer-implemented method of claim 1,wherein the aggregated level is one of the geographic regions within theUnited States of America or a state.
 5. The computer-implemented methodof claim 1, wherein the disaggregated level is a county, a zip codearea, or a census tract.
 6. A system for adjusting a weighted repeatsales index, the system comprising: means for calculating data forestimating adjustments from aggregated levels to disaggregated levels bymarking a data element representing a first transaction to a dataelement representing a second transaction using a processor-implementedrepeat sales house price index function at an aggregated level; meansfor determining, using the calculated data, an estimate of deviationbetween the repeat sales house price index at the aggregated level and arepeat sales house price index at a disaggregated level; and means forcalculating the repeat sales house price index at the disaggregatedlevel based on the determined estimate of the deviation from theaggregated level, wherein the adjusted weighted repeat sales indexproduces an estimated index function.
 7. The system of claim 6, whereinthe deviations are estimated as a function of time spline variables. 8.The system of claim 7, wherein an increase of a number of knot points ofthe time spline increases the accuracy of the estimated index function.9. The system of claim 6, wherein the aggregated level is one of thegeographic regions within the United States of America or a state. 10.The system of claim 6, wherein the disaggregated level is a county, azip code area, or a census tract.
 11. A non-transitory computer-readablemedium storing program instructions for performing, when executed by aprocessor, a method for adjusting a weighted repeat sales index, themethod comprising: calculating data for estimating adjustments fromaggregated levels to disaggregated levels by marking a data elementrepresenting a first transaction to a data element representing a secondtransaction using a processor-implemented repeat sales house price indexfunction at an aggregated level; determining, using the calculated data,an estimate of deviation between the repeat sales house price index atthe aggregated level and a repeat sales house price index at adisaggregated level; and calculating the repeat sales house price indexat the disaggregated level based on the determined estimate of thedeviation from the aggregated level, wherein the adjusted weightedrepeat sales index produces an estimated index function.
 12. Thecomputer-readable medium of claim 11, wherein the deviations areestimated as a function of time spline variables.
 13. Thecomputer-readable medium of claim 12, wherein an increase of a number ofknot points of the time spline increases the accuracy of the estimatedindex function.
 14. The computer-readable medium of claim 11, whereinthe aggregated level is one of the geographic regions within the UnitedStates of America or a state.
 15. The computer-readable medium of claim11, wherein the disaggregated level is a county, a zip code area, or acensus tract.